@inproceedings{xie-etal-2025-coalign,
title = "{C}o{A}lign: Uncertainty Calibration of {LLM} for Geospatial Repartition",
author = "Xie, Zejun and
Hong, Zhiqing and
Lyu, Wenjun and
Wang, Haotian and
Wang, Guang and
Zhang, Desheng",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.19/",
doi = "10.18653/v1/2025.acl-industry.19",
pages = "254--263",
ISBN = "979-8-89176-288-6",
abstract = "With the rapid expansion of e-commerce and continuous urban evolution, Geospatial Repartition, dividing geographical regions into delivery zones, is essential to optimize various objectives, e.g., on-time delivery rate, for last-mile delivery. Recently, large language models (LLMs) have offered promising capabilities for integrating diverse contextual information that is beneficial for geospatial repartition. However, given the inherent uncertainty in LLMs, adapting them to practical usage in real-world repartition is nontrivial. Thus, we introduce CoAlign, a novel three-stage framework that calibrates LLM uncertainty to enable robust geospatial repartition by transforming the task into a ranking problem, integrating historical data with LLM-generated candidates. It first generates explainable candidate partitions with a multi-criteria strategy and then designs a novel conformal method to rank these candidates relative to historical partitions with coverage guarantees. Finally, CoAlign delivers candidates through an interactive decision support system. Extensive evaluation with real-world data shows that CoAlign effectively calibrates LLM uncertainty and generates partitions that better align with human feedback. Moreover, we have deployed CoAlign in one of the world{'}s largest logistics companies, significantly enhancing their delivery operations by increasing candidate acceptance rates by 300{\%} and improving on-time delivery rates by 3{\%}. Our work provides a novel angle to address industrial geospatial decision-making tasks by calibrating LLM uncertainty."
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<abstract>With the rapid expansion of e-commerce and continuous urban evolution, Geospatial Repartition, dividing geographical regions into delivery zones, is essential to optimize various objectives, e.g., on-time delivery rate, for last-mile delivery. Recently, large language models (LLMs) have offered promising capabilities for integrating diverse contextual information that is beneficial for geospatial repartition. However, given the inherent uncertainty in LLMs, adapting them to practical usage in real-world repartition is nontrivial. Thus, we introduce CoAlign, a novel three-stage framework that calibrates LLM uncertainty to enable robust geospatial repartition by transforming the task into a ranking problem, integrating historical data with LLM-generated candidates. It first generates explainable candidate partitions with a multi-criteria strategy and then designs a novel conformal method to rank these candidates relative to historical partitions with coverage guarantees. Finally, CoAlign delivers candidates through an interactive decision support system. Extensive evaluation with real-world data shows that CoAlign effectively calibrates LLM uncertainty and generates partitions that better align with human feedback. Moreover, we have deployed CoAlign in one of the world’s largest logistics companies, significantly enhancing their delivery operations by increasing candidate acceptance rates by 300% and improving on-time delivery rates by 3%. Our work provides a novel angle to address industrial geospatial decision-making tasks by calibrating LLM uncertainty.</abstract>
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%0 Conference Proceedings
%T CoAlign: Uncertainty Calibration of LLM for Geospatial Repartition
%A Xie, Zejun
%A Hong, Zhiqing
%A Lyu, Wenjun
%A Wang, Haotian
%A Wang, Guang
%A Zhang, Desheng
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F xie-etal-2025-coalign
%X With the rapid expansion of e-commerce and continuous urban evolution, Geospatial Repartition, dividing geographical regions into delivery zones, is essential to optimize various objectives, e.g., on-time delivery rate, for last-mile delivery. Recently, large language models (LLMs) have offered promising capabilities for integrating diverse contextual information that is beneficial for geospatial repartition. However, given the inherent uncertainty in LLMs, adapting them to practical usage in real-world repartition is nontrivial. Thus, we introduce CoAlign, a novel three-stage framework that calibrates LLM uncertainty to enable robust geospatial repartition by transforming the task into a ranking problem, integrating historical data with LLM-generated candidates. It first generates explainable candidate partitions with a multi-criteria strategy and then designs a novel conformal method to rank these candidates relative to historical partitions with coverage guarantees. Finally, CoAlign delivers candidates through an interactive decision support system. Extensive evaluation with real-world data shows that CoAlign effectively calibrates LLM uncertainty and generates partitions that better align with human feedback. Moreover, we have deployed CoAlign in one of the world’s largest logistics companies, significantly enhancing their delivery operations by increasing candidate acceptance rates by 300% and improving on-time delivery rates by 3%. Our work provides a novel angle to address industrial geospatial decision-making tasks by calibrating LLM uncertainty.
%R 10.18653/v1/2025.acl-industry.19
%U https://aclanthology.org/2025.acl-industry.19/
%U https://doi.org/10.18653/v1/2025.acl-industry.19
%P 254-263
Markdown (Informal)
[CoAlign: Uncertainty Calibration of LLM for Geospatial Repartition](https://aclanthology.org/2025.acl-industry.19/) (Xie et al., ACL 2025)
ACL
- Zejun Xie, Zhiqing Hong, Wenjun Lyu, Haotian Wang, Guang Wang, and Desheng Zhang. 2025. CoAlign: Uncertainty Calibration of LLM for Geospatial Repartition. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 254–263, Vienna, Austria. Association for Computational Linguistics.